Overview

Brought to you by YData

Dataset statistics

Number of variables31
Number of observations8220
Missing cells33085
Missing cells (%)13.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 MiB
Average record size in memory248.0 B

Variable types

Text7
Categorical8
Numeric14
DateTime2

Alerts

origin has constant value "semi-synthetic" Constant
uc_repo has constant value "SAREC-Lab/sUAS-UseCases" Constant
uc_ai_model has constant value "gpt-3.5-turbo-0125" Constant
seq_ai_model has constant value "gpt-3.5-turbo-0125" Constant
seq_actors is highly overall correlated with uc_source_urlHigh correlation
seq_id is highly overall correlated with uc_id and 1 other fieldsHigh correlation
seq_num_of_participats is highly overall correlated with uc_source_urlHigh correlation
seq_origin is highly overall correlated with seq_temperature and 6 other fieldsHigh correlation
seq_temperature is highly overall correlated with seq_origin and 3 other fieldsHigh correlation
seq_top_p is highly overall correlated with seq_origin and 3 other fieldsHigh correlation
uc_actors is highly overall correlated with uc_source_urlHigh correlation
uc_id is highly overall correlated with seq_id and 1 other fieldsHigh correlation
uc_num_of_alt_scenarions is highly overall correlated with uc_num_of_err_scenarions and 1 other fieldsHigh correlation
uc_num_of_err_scenarions is highly overall correlated with seq_origin and 5 other fieldsHigh correlation
uc_num_of_steps is highly overall correlated with uc_source_urlHigh correlation
uc_origin is highly overall correlated with seq_origin and 6 other fieldsHigh correlation
uc_source_url is highly overall correlated with seq_actors and 11 other fieldsHigh correlation
uc_temperature is highly overall correlated with seq_origin and 2 other fieldsHigh correlation
uc_top_p is highly overall correlated with seq_origin and 2 other fieldsHigh correlation
uc_origin is highly imbalanced (84.0%) Imbalance
seq_origin is highly imbalanced (84.0%) Imbalance
seq_num_of_opt is highly imbalanced (93.2%) Imbalance
uc_source_url has 8028 (97.7%) missing values Missing
uc_repo has 8028 (97.7%) missing values Missing
uc_ai_model has 192 (2.3%) missing values Missing
seq_date has 766 (9.3%) missing values Missing
seq_source_url has 192 (2.3%) missing values Missing
seq_repo has 192 (2.3%) missing values Missing
seq_ai_model has 8028 (97.7%) missing values Missing
seq_title has 7659 (93.2%) missing values Missing
uc_source_url is uniformly distributed Uniform
uc_temperature has 192 (2.3%) zeros Zeros
uc_top_p has 192 (2.3%) zeros Zeros
uc_num_of_alt_scenarions has 7553 (91.9%) zeros Zeros
uc_num_of_err_scenarions has 7733 (94.1%) zeros Zeros
seq_temperature has 8028 (97.7%) zeros Zeros
seq_top_p has 8028 (97.7%) zeros Zeros
seq_actors has 581 (7.1%) zeros Zeros
seq_num_of_alt has 6292 (76.5%) zeros Zeros
seq_num_of_loop has 7757 (94.4%) zeros Zeros

Reproduction

Analysis started2025-04-29 09:36:48.282151
Analysis finished2025-04-29 09:37:01.690862
Duration13.41 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

name
Text

Distinct2924
Distinct (%)35.6%
Missing0
Missing (%)0.0%
Memory size64.3 KiB
2025-04-29T11:37:01.822263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length100
Median length67
Mean length23.727981
Min length4

Characters and Unicode

Total characters195044
Distinct characters222
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1746 ?
Unique (%)21.2%

Sample

1st rowRiver Search and Rescue
2nd rowRiver Search and Rescue
3rd rowRiver Search and Rescue
4th rowRiver Search and Rescue
5th rowRiver Search and Rescue
ValueCountFrequency (%)
and 626
 
2.2%
manage 614
 
2.2%
user 567
 
2.0%
view 460
 
1.7%
process 360
 
1.3%
a 356
 
1.3%
data 339
 
1.2%
create 322
 
1.2%
new 285
 
1.0%
update 278
 
1.0%
Other values (2076) 23646
84.9%
2025-04-29T11:37:02.031876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 21784
 
11.2%
19652
 
10.1%
a 13752
 
7.1%
t 13700
 
7.0%
n 12186
 
6.2%
i 11957
 
6.1%
o 11212
 
5.7%
r 10606
 
5.4%
s 9972
 
5.1%
c 6166
 
3.2%
Other values (212) 64057
32.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 195044
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 21784
 
11.2%
19652
 
10.1%
a 13752
 
7.1%
t 13700
 
7.0%
n 12186
 
6.2%
i 11957
 
6.1%
o 11212
 
5.7%
r 10606
 
5.4%
s 9972
 
5.1%
c 6166
 
3.2%
Other values (212) 64057
32.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 195044
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 21784
 
11.2%
19652
 
10.1%
a 13752
 
7.1%
t 13700
 
7.0%
n 12186
 
6.2%
i 11957
 
6.1%
o 11212
 
5.7%
r 10606
 
5.4%
s 9972
 
5.1%
c 6166
 
3.2%
Other values (212) 64057
32.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 195044
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 21784
 
11.2%
19652
 
10.1%
a 13752
 
7.1%
t 13700
 
7.0%
n 12186
 
6.2%
i 11957
 
6.1%
o 11212
 
5.7%
r 10606
 
5.4%
s 9972
 
5.1%
c 6166
 
3.2%
Other values (212) 64057
32.8%

origin
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size64.3 KiB
semi-synthetic
8220 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters115080
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsemi-synthetic
2nd rowsemi-synthetic
3rd rowsemi-synthetic
4th rowsemi-synthetic
5th rowsemi-synthetic

Common Values

ValueCountFrequency (%)
semi-synthetic 8220
100.0%

Length

2025-04-29T11:37:02.080889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-29T11:37:02.118055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
semi-synthetic 8220
100.0%

Most occurring characters

ValueCountFrequency (%)
s 16440
14.3%
e 16440
14.3%
i 16440
14.3%
t 16440
14.3%
- 8220
7.1%
m 8220
7.1%
y 8220
7.1%
n 8220
7.1%
h 8220
7.1%
c 8220
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 115080
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 16440
14.3%
e 16440
14.3%
i 16440
14.3%
t 16440
14.3%
- 8220
7.1%
m 8220
7.1%
y 8220
7.1%
n 8220
7.1%
h 8220
7.1%
c 8220
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 115080
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 16440
14.3%
e 16440
14.3%
i 16440
14.3%
t 16440
14.3%
- 8220
7.1%
m 8220
7.1%
y 8220
7.1%
n 8220
7.1%
h 8220
7.1%
c 8220
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 115080
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 16440
14.3%
e 16440
14.3%
i 16440
14.3%
t 16440
14.3%
- 8220
7.1%
m 8220
7.1%
y 8220
7.1%
n 8220
7.1%
h 8220
7.1%
c 8220
7.1%

uc_id
Real number (ℝ)

High correlation 

Distinct8028
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3920.979
Minimum1
Maximum8028
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.3 KiB
2025-04-29T11:37:02.169579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile219.95
Q11863.75
median3918.5
Q35973.25
95-th percentile7617.05
Maximum8028
Range8027
Interquartile range (IQR)4109.5

Descriptive statistics

Standard deviation2368.8958
Coefficient of variation (CV)0.60415929
Kurtosis-1.2071574
Mean3920.979
Median Absolute Deviation (MAD)2055
Skewness0.0057049395
Sum32230447
Variance5611667.5
MonotonicityNot monotonic
2025-04-29T11:37:02.243476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 11
 
0.1%
12 11
 
0.1%
14 11
 
0.1%
15 11
 
0.1%
16 11
 
0.1%
17 11
 
0.1%
18 11
 
0.1%
19 11
 
0.1%
20 11
 
0.1%
6 11
 
0.1%
Other values (8018) 8110
98.7%
ValueCountFrequency (%)
1 10
0.1%
2 11
0.1%
3 10
0.1%
4 10
0.1%
5 11
0.1%
6 11
0.1%
7 10
0.1%
8 10
0.1%
9 11
0.1%
10 10
0.1%
ValueCountFrequency (%)
8028 1
< 0.1%
8027 1
< 0.1%
8026 1
< 0.1%
8025 1
< 0.1%
8024 1
< 0.1%
8023 1
< 0.1%
8022 1
< 0.1%
8021 1
< 0.1%
8020 1
< 0.1%
8019 1
< 0.1%
Distinct8048
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Memory size64.3 KiB
2025-04-29T11:37:02.340142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length23
Median length23
Mean length22.813139
Min length15

Characters and Unicode

Total characters187524
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8028 ?
Unique (%)97.7%

Sample

1st rowuc\00001_uc.xml
2nd rowuc\00001_uc.xml
3rd rowuc\00001_uc.xml
4th rowuc\00001_uc.xml
5th rowuc\00001_uc.xml
ValueCountFrequency (%)
uc\00020_uc.xml 10
 
0.1%
uc\00016_uc.xml 10
 
0.1%
uc\00002_uc.xml 10
 
0.1%
uc\00005_uc.xml 10
 
0.1%
uc\00006_uc.xml 10
 
0.1%
uc\00009_uc.xml 10
 
0.1%
uc\00011_uc.xml 10
 
0.1%
uc\00012_uc.xml 10
 
0.1%
uc\00014_uc.xml 10
 
0.1%
uc\00017_uc.xml 10
 
0.1%
Other values (8038) 8120
98.8%
2025-04-29T11:37:02.475142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 24276
12.9%
c 16440
 
8.8%
u 16440
 
8.8%
g 16056
 
8.6%
n 16056
 
8.6%
e 16056
 
8.6%
0 12146
 
6.5%
l 8220
 
4.4%
\ 8220
 
4.4%
x 8220
 
4.4%
Other values (11) 45394
24.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 187524
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
_ 24276
12.9%
c 16440
 
8.8%
u 16440
 
8.8%
g 16056
 
8.6%
n 16056
 
8.6%
e 16056
 
8.6%
0 12146
 
6.5%
l 8220
 
4.4%
\ 8220
 
4.4%
x 8220
 
4.4%
Other values (11) 45394
24.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 187524
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
_ 24276
12.9%
c 16440
 
8.8%
u 16440
 
8.8%
g 16056
 
8.6%
n 16056
 
8.6%
e 16056
 
8.6%
0 12146
 
6.5%
l 8220
 
4.4%
\ 8220
 
4.4%
x 8220
 
4.4%
Other values (11) 45394
24.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 187524
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
_ 24276
12.9%
c 16440
 
8.8%
u 16440
 
8.8%
g 16056
 
8.6%
n 16056
 
8.6%
e 16056
 
8.6%
0 12146
 
6.5%
l 8220
 
4.4%
\ 8220
 
4.4%
x 8220
 
4.4%
Other values (11) 45394
24.2%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size64.3 KiB
Minimum2020-12-19 00:00:00
Maximum2025-04-29 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-04-29T11:37:02.515376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:37:02.560934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=3)

uc_origin
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size64.3 KiB
chatgpt
8028 
github
 
192

Length

Max length7
Median length7
Mean length6.9766423
Min length6

Characters and Unicode

Total characters57348
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgithub
2nd rowgithub
3rd rowgithub
4th rowgithub
5th rowgithub

Common Values

ValueCountFrequency (%)
chatgpt 8028
97.7%
github 192
 
2.3%

Length

2025-04-29T11:37:02.613934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-29T11:37:02.645941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
chatgpt 8028
97.7%
github 192
 
2.3%

Most occurring characters

ValueCountFrequency (%)
t 16248
28.3%
g 8220
14.3%
h 8220
14.3%
c 8028
14.0%
a 8028
14.0%
p 8028
14.0%
i 192
 
0.3%
u 192
 
0.3%
b 192
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 57348
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 16248
28.3%
g 8220
14.3%
h 8220
14.3%
c 8028
14.0%
a 8028
14.0%
p 8028
14.0%
i 192
 
0.3%
u 192
 
0.3%
b 192
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 57348
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 16248
28.3%
g 8220
14.3%
h 8220
14.3%
c 8028
14.0%
a 8028
14.0%
p 8028
14.0%
i 192
 
0.3%
u 192
 
0.3%
b 192
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 57348
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 16248
28.3%
g 8220
14.3%
h 8220
14.3%
c 8028
14.0%
a 8028
14.0%
p 8028
14.0%
i 192
 
0.3%
u 192
 
0.3%
b 192
 
0.3%

uc_source_url
Categorical

High correlation  Missing  Uniform 

Distinct20
Distinct (%)10.4%
Missing8028
Missing (%)97.7%
Memory size64.3 KiB
https://raw.githubusercontent.com/SAREC-Lab/sUAS-UseCases/refs/heads/SPLC-2020/usecases/main/EnvironmentalSampling.md
 
10
https://raw.githubusercontent.com/SAREC-Lab/sUAS-UseCases/refs/heads/SPLC-2020/usecases/supporting/FlyToDestination.md
 
10
https://raw.githubusercontent.com/SAREC-Lab/sUAS-UseCases/refs/heads/SPLC-2020/usecases/supporting/ActivateAndArm.md
 
10
https://raw.githubusercontent.com/SAREC-Lab/sUAS-UseCases/refs/heads/SPLC-2020/usecases/main/ItemDelivery.md
 
10
https://raw.githubusercontent.com/SAREC-Lab/sUAS-UseCases/refs/heads/SPLC-2020/usecases/supporting/VictimConfirmation.md
 
10
Other values (15)
142 

Length

Max length127
Median length120
Mean length117.57292
Min length107

Characters and Unicode

Total characters22574
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhttps://raw.githubusercontent.com/SAREC-Lab/sUAS-UseCases/refs/heads/SPLC-2020/usecases/main/RiverRescue.md
2nd rowhttps://raw.githubusercontent.com/SAREC-Lab/sUAS-UseCases/refs/heads/SPLC-2020/usecases/main/RiverRescue.md
3rd rowhttps://raw.githubusercontent.com/SAREC-Lab/sUAS-UseCases/refs/heads/SPLC-2020/usecases/main/RiverRescue.md
4th rowhttps://raw.githubusercontent.com/SAREC-Lab/sUAS-UseCases/refs/heads/SPLC-2020/usecases/main/RiverRescue.md
5th rowhttps://raw.githubusercontent.com/SAREC-Lab/sUAS-UseCases/refs/heads/SPLC-2020/usecases/main/RiverRescue.md

Common Values

ValueCountFrequency (%)
https://raw.githubusercontent.com/SAREC-Lab/sUAS-UseCases/refs/heads/SPLC-2020/usecases/main/EnvironmentalSampling.md 10
 
0.1%
https://raw.githubusercontent.com/SAREC-Lab/sUAS-UseCases/refs/heads/SPLC-2020/usecases/supporting/FlyToDestination.md 10
 
0.1%
https://raw.githubusercontent.com/SAREC-Lab/sUAS-UseCases/refs/heads/SPLC-2020/usecases/supporting/ActivateAndArm.md 10
 
0.1%
https://raw.githubusercontent.com/SAREC-Lab/sUAS-UseCases/refs/heads/SPLC-2020/usecases/main/ItemDelivery.md 10
 
0.1%
https://raw.githubusercontent.com/SAREC-Lab/sUAS-UseCases/refs/heads/SPLC-2020/usecases/supporting/VictimConfirmation.md 10
 
0.1%
https://raw.githubusercontent.com/SAREC-Lab/sUAS-UseCases/refs/heads/SPLC-2020/usecases/general_exceptions/LossOfSignal.md 10
 
0.1%
https://raw.githubusercontent.com/SAREC-Lab/sUAS-UseCases/refs/heads/SPLC-2020/usecases/general_exceptions/GeofenceIncursion.md 10
 
0.1%
https://raw.githubusercontent.com/SAREC-Lab/sUAS-UseCases/refs/heads/SPLC-2020/usecases/supporting/CollectAndAnalyseSample.md 10
 
0.1%
https://raw.githubusercontent.com/SAREC-Lab/sUAS-UseCases/refs/heads/SPLC-2020/usecases/supporting/LeaseAirspace.md 10
 
0.1%
https://raw.githubusercontent.com/SAREC-Lab/sUAS-UseCases/refs/heads/SPLC-2020/usecases/supporting/SynchronizedTakeoff.md 10
 
0.1%
Other values (10) 92
 
1.1%
(Missing) 8028
97.7%

Length

2025-04-29T11:37:02.693479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://raw.githubusercontent.com/sarec-lab/suas-usecases/refs/heads/splc-2020/usecases/main/environmentalsampling.md 10
 
5.2%
https://raw.githubusercontent.com/sarec-lab/suas-usecases/refs/heads/splc-2020/usecases/supporting/flytodestination.md 10
 
5.2%
https://raw.githubusercontent.com/sarec-lab/suas-usecases/refs/heads/splc-2020/usecases/supporting/activateandarm.md 10
 
5.2%
https://raw.githubusercontent.com/sarec-lab/suas-usecases/refs/heads/splc-2020/usecases/main/itemdelivery.md 10
 
5.2%
https://raw.githubusercontent.com/sarec-lab/suas-usecases/refs/heads/splc-2020/usecases/supporting/victimconfirmation.md 10
 
5.2%
https://raw.githubusercontent.com/sarec-lab/suas-usecases/refs/heads/splc-2020/usecases/general_exceptions/lossofsignal.md 10
 
5.2%
https://raw.githubusercontent.com/sarec-lab/suas-usecases/refs/heads/splc-2020/usecases/general_exceptions/geofenceincursion.md 10
 
5.2%
https://raw.githubusercontent.com/sarec-lab/suas-usecases/refs/heads/splc-2020/usecases/supporting/collectandanalysesample.md 10
 
5.2%
https://raw.githubusercontent.com/sarec-lab/suas-usecases/refs/heads/splc-2020/usecases/supporting/leaseairspace.md 10
 
5.2%
https://raw.githubusercontent.com/sarec-lab/suas-usecases/refs/heads/splc-2020/usecases/supporting/synchronizedtakeoff.md 10
 
5.2%
Other values (10) 92
47.9%

Most occurring characters

ValueCountFrequency (%)
s 2370
 
10.5%
e 1970
 
8.7%
/ 1920
 
8.5%
t 1317
 
5.8%
a 1236
 
5.5%
r 901
 
4.0%
n 847
 
3.8%
u 764
 
3.4%
c 739
 
3.3%
o 706
 
3.1%
Other values (38) 9804
43.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22574
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 2370
 
10.5%
e 1970
 
8.7%
/ 1920
 
8.5%
t 1317
 
5.8%
a 1236
 
5.5%
r 901
 
4.0%
n 847
 
3.8%
u 764
 
3.4%
c 739
 
3.3%
o 706
 
3.1%
Other values (38) 9804
43.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22574
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 2370
 
10.5%
e 1970
 
8.7%
/ 1920
 
8.5%
t 1317
 
5.8%
a 1236
 
5.5%
r 901
 
4.0%
n 847
 
3.8%
u 764
 
3.4%
c 739
 
3.3%
o 706
 
3.1%
Other values (38) 9804
43.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22574
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 2370
 
10.5%
e 1970
 
8.7%
/ 1920
 
8.5%
t 1317
 
5.8%
a 1236
 
5.5%
r 901
 
4.0%
n 847
 
3.8%
u 764
 
3.4%
c 739
 
3.3%
o 706
 
3.1%
Other values (38) 9804
43.4%

uc_repo
Categorical

Constant  Missing 

Distinct1
Distinct (%)0.5%
Missing8028
Missing (%)97.7%
Memory size64.3 KiB
SAREC-Lab/sUAS-UseCases
192 

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters4416
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSAREC-Lab/sUAS-UseCases
2nd rowSAREC-Lab/sUAS-UseCases
3rd rowSAREC-Lab/sUAS-UseCases
4th rowSAREC-Lab/sUAS-UseCases
5th rowSAREC-Lab/sUAS-UseCases

Common Values

ValueCountFrequency (%)
SAREC-Lab/sUAS-UseCases 192
 
2.3%
(Missing) 8028
97.7%

Length

2025-04-29T11:37:02.745578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-29T11:37:02.773578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sarec-lab/suas-usecases 192
100.0%

Most occurring characters

ValueCountFrequency (%)
s 768
17.4%
A 384
8.7%
C 384
8.7%
- 384
8.7%
S 384
8.7%
e 384
8.7%
a 384
8.7%
U 384
8.7%
R 192
 
4.3%
E 192
 
4.3%
Other values (3) 576
13.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4416
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 768
17.4%
A 384
8.7%
C 384
8.7%
- 384
8.7%
S 384
8.7%
e 384
8.7%
a 384
8.7%
U 384
8.7%
R 192
 
4.3%
E 192
 
4.3%
Other values (3) 576
13.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4416
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 768
17.4%
A 384
8.7%
C 384
8.7%
- 384
8.7%
S 384
8.7%
e 384
8.7%
a 384
8.7%
U 384
8.7%
R 192
 
4.3%
E 192
 
4.3%
Other values (3) 576
13.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4416
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 768
17.4%
A 384
8.7%
C 384
8.7%
- 384
8.7%
S 384
8.7%
e 384
8.7%
a 384
8.7%
U 384
8.7%
R 192
 
4.3%
E 192
 
4.3%
Other values (3) 576
13.0%

uc_ai_model
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing192
Missing (%)2.3%
Memory size64.3 KiB
gpt-3.5-turbo-0125
8028 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters144504
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgpt-3.5-turbo-0125
2nd rowgpt-3.5-turbo-0125
3rd rowgpt-3.5-turbo-0125
4th rowgpt-3.5-turbo-0125
5th rowgpt-3.5-turbo-0125

Common Values

ValueCountFrequency (%)
gpt-3.5-turbo-0125 8028
97.7%
(Missing) 192
 
2.3%

Length

2025-04-29T11:37:02.809578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-29T11:37:02.837578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
gpt-3.5-turbo-0125 8028
100.0%

Most occurring characters

ValueCountFrequency (%)
- 24084
16.7%
t 16056
11.1%
5 16056
11.1%
p 8028
 
5.6%
3 8028
 
5.6%
g 8028
 
5.6%
. 8028
 
5.6%
u 8028
 
5.6%
r 8028
 
5.6%
b 8028
 
5.6%
Other values (4) 32112
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 144504
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 24084
16.7%
t 16056
11.1%
5 16056
11.1%
p 8028
 
5.6%
3 8028
 
5.6%
g 8028
 
5.6%
. 8028
 
5.6%
u 8028
 
5.6%
r 8028
 
5.6%
b 8028
 
5.6%
Other values (4) 32112
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 144504
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 24084
16.7%
t 16056
11.1%
5 16056
11.1%
p 8028
 
5.6%
3 8028
 
5.6%
g 8028
 
5.6%
. 8028
 
5.6%
u 8028
 
5.6%
r 8028
 
5.6%
b 8028
 
5.6%
Other values (4) 32112
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 144504
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 24084
16.7%
t 16056
11.1%
5 16056
11.1%
p 8028
 
5.6%
3 8028
 
5.6%
g 8028
 
5.6%
. 8028
 
5.6%
u 8028
 
5.6%
r 8028
 
5.6%
b 8028
 
5.6%
Other values (4) 32112
22.2%

uc_temperature
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.96510949
Minimum0
Maximum1.4
Zeros192
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size64.3 KiB
2025-04-29T11:37:02.864577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.6
Q11
median1
Q31
95-th percentile1.4
Maximum1.4
Range1.4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.24182293
Coefficient of variation (CV)0.25056528
Kurtosis4.3607922
Mean0.96510949
Median Absolute Deviation (MAD)0
Skewness-1.3361649
Sum7933.2
Variance0.058478331
MonotonicityNot monotonic
2025-04-29T11:37:02.905789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 4939
60.1%
0.8 841
 
10.2%
0.6 835
 
10.2%
1.2 789
 
9.6%
1.4 624
 
7.6%
0 192
 
2.3%
ValueCountFrequency (%)
0 192
 
2.3%
0.6 835
 
10.2%
0.8 841
 
10.2%
1 4939
60.1%
1.2 789
 
9.6%
1.4 624
 
7.6%
ValueCountFrequency (%)
1.4 624
 
7.6%
1.2 789
 
9.6%
1 4939
60.1%
0.8 841
 
10.2%
0.6 835
 
10.2%
0 192
 
2.3%

uc_top_p
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.77766423
Minimum0
Maximum1
Zeros192
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size64.3 KiB
2025-04-29T11:37:02.949835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q10.6
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.30505717
Coefficient of variation (CV)0.39227363
Kurtosis-0.32935852
Mean0.77766423
Median Absolute Deviation (MAD)0
Skewness-1.0449792
Sum6392.4
Variance0.09305988
MonotonicityNot monotonic
2025-04-29T11:37:02.989835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 4743
57.7%
0.8 833
 
10.1%
0.6 828
 
10.1%
0.2 817
 
9.9%
0.4 807
 
9.8%
0 192
 
2.3%
ValueCountFrequency (%)
0 192
 
2.3%
0.2 817
 
9.9%
0.4 807
 
9.8%
0.6 828
 
10.1%
0.8 833
 
10.1%
1 4743
57.7%
ValueCountFrequency (%)
1 4743
57.7%
0.8 833
 
10.1%
0.6 828
 
10.1%
0.4 807
 
9.8%
0.2 817
 
9.9%
0 192
 
2.3%
Distinct3002
Distinct (%)36.5%
Missing0
Missing (%)0.0%
Memory size64.3 KiB
2025-04-29T11:37:03.117015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length100
Median length66
Mean length23.38674
Min length4

Characters and Unicode

Total characters192239
Distinct characters221
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1809 ?
Unique (%)22.0%

Sample

1st rowRiver Search and Rescue
2nd rowRiver Search and Rescue
3rd rowRiver Search and Rescue
4th rowRiver Search and Rescue
5th rowRiver Search and Rescue
ValueCountFrequency (%)
and 630
 
2.3%
manage 619
 
2.2%
user 576
 
2.1%
view 460
 
1.7%
process 375
 
1.4%
a 361
 
1.3%
data 351
 
1.3%
create 323
 
1.2%
new 289
 
1.0%
update 280
 
1.0%
Other values (2090) 23421
84.6%
2025-04-29T11:37:03.321626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 21445
 
11.2%
19484
 
10.1%
t 13561
 
7.1%
a 13541
 
7.0%
n 12036
 
6.3%
i 11860
 
6.2%
o 11161
 
5.8%
r 10471
 
5.4%
s 9914
 
5.2%
c 6062
 
3.2%
Other values (211) 62704
32.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 192239
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 21445
 
11.2%
19484
 
10.1%
t 13561
 
7.1%
a 13541
 
7.0%
n 12036
 
6.3%
i 11860
 
6.2%
o 11161
 
5.8%
r 10471
 
5.4%
s 9914
 
5.2%
c 6062
 
3.2%
Other values (211) 62704
32.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 192239
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 21445
 
11.2%
19484
 
10.1%
t 13561
 
7.1%
a 13541
 
7.0%
n 12036
 
6.3%
i 11860
 
6.2%
o 11161
 
5.8%
r 10471
 
5.4%
s 9914
 
5.2%
c 6062
 
3.2%
Other values (211) 62704
32.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 192239
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 21445
 
11.2%
19484
 
10.1%
t 13561
 
7.1%
a 13541
 
7.0%
n 12036
 
6.3%
i 11860
 
6.2%
o 11161
 
5.8%
r 10471
 
5.4%
s 9914
 
5.2%
c 6062
 
3.2%
Other values (211) 62704
32.6%

uc_actors
Real number (ℝ)

High correlation 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0265207
Minimum0
Maximum19
Zeros31
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size64.3 KiB
2025-04-29T11:37:03.368008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q35
95-th percentile7
Maximum19
Range19
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.3736144
Coefficient of variation (CV)0.78427166
Kurtosis1.2689957
Mean3.0265207
Median Absolute Deviation (MAD)1
Skewness1.1529331
Sum24878
Variance5.6340453
MonotonicityNot monotonic
2025-04-29T11:37:03.416009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 3583
43.6%
4 976
 
11.9%
5 897
 
10.9%
3 752
 
9.1%
2 708
 
8.6%
6 538
 
6.5%
7 350
 
4.3%
8 160
 
1.9%
9 117
 
1.4%
10 51
 
0.6%
Other values (6) 88
 
1.1%
ValueCountFrequency (%)
0 31
 
0.4%
1 3583
43.6%
2 708
 
8.6%
3 752
 
9.1%
4 976
 
11.9%
5 897
 
10.9%
6 538
 
6.5%
7 350
 
4.3%
8 160
 
1.9%
9 117
 
1.4%
ValueCountFrequency (%)
19 1
 
< 0.1%
14 13
 
0.2%
13 16
 
0.2%
12 7
 
0.1%
11 20
 
0.2%
10 51
 
0.6%
9 117
 
1.4%
8 160
 
1.9%
7 350
4.3%
6 538
6.5%

uc_num_of_steps
Real number (ℝ)

High correlation 

Distinct51
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.2013382
Minimum1
Maximum101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.3 KiB
2025-04-29T11:37:03.479173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q15.75
median8
Q311
95-th percentile20
Maximum101
Range100
Interquartile range (IQR)5.25

Descriptive statistics

Standard deviation5.7951168
Coefficient of variation (CV)0.62981238
Kurtosis14.377649
Mean9.2013382
Median Absolute Deviation (MAD)3
Skewness2.4393765
Sum75635
Variance33.583378
MonotonicityNot monotonic
2025-04-29T11:37:03.545385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 848
10.3%
6 847
10.3%
4 828
10.1%
7 670
 
8.2%
9 653
 
7.9%
10 642
 
7.8%
5 534
 
6.5%
11 500
 
6.1%
12 480
 
5.8%
3 362
 
4.4%
Other values (41) 1856
22.6%
ValueCountFrequency (%)
1 90
 
1.1%
2 241
 
2.9%
3 362
4.4%
4 828
10.1%
5 534
6.5%
6 847
10.3%
7 670
8.2%
8 848
10.3%
9 653
7.9%
10 642
7.8%
ValueCountFrequency (%)
101 1
 
< 0.1%
59 1
 
< 0.1%
54 1
 
< 0.1%
52 1
 
< 0.1%
50 1
 
< 0.1%
48 1
 
< 0.1%
45 1
 
< 0.1%
44 1
 
< 0.1%
43 1
 
< 0.1%
42 3
< 0.1%

uc_num_of_alt_scenarions
Real number (ℝ)

High correlation  Zeros 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1203163
Minimum0
Maximum8
Zeros7553
Zeros (%)91.9%
Negative0
Negative (%)0.0%
Memory size64.3 KiB
2025-04-29T11:37:03.595308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.46979675
Coefficient of variation (CV)3.9046808
Kurtosis38.249997
Mean0.1203163
Median Absolute Deviation (MAD)0
Skewness5.3120662
Sum989
Variance0.22070899
MonotonicityNot monotonic
2025-04-29T11:37:03.639308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 7553
91.9%
1 445
 
5.4%
2 155
 
1.9%
3 43
 
0.5%
4 20
 
0.2%
6 2
 
< 0.1%
8 1
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 7553
91.9%
1 445
 
5.4%
2 155
 
1.9%
3 43
 
0.5%
4 20
 
0.2%
5 1
 
< 0.1%
6 2
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
6 2
 
< 0.1%
5 1
 
< 0.1%
4 20
 
0.2%
3 43
 
0.5%
2 155
 
1.9%
1 445
 
5.4%
0 7553
91.9%

uc_num_of_err_scenarions
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.092092457
Minimum0
Maximum5
Zeros7733
Zeros (%)94.1%
Negative0
Negative (%)0.0%
Memory size64.3 KiB
2025-04-29T11:37:03.682322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.44028854
Coefficient of variation (CV)4.7809403
Kurtosis49.368811
Mean0.092092457
Median Absolute Deviation (MAD)0
Skewness6.4482618
Sum757
Variance0.193854
MonotonicityNot monotonic
2025-04-29T11:37:03.720517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 7733
94.1%
1 345
 
4.2%
2 58
 
0.7%
3 51
 
0.6%
4 22
 
0.3%
5 11
 
0.1%
ValueCountFrequency (%)
0 7733
94.1%
1 345
 
4.2%
2 58
 
0.7%
3 51
 
0.6%
4 22
 
0.3%
5 11
 
0.1%
ValueCountFrequency (%)
5 11
 
0.1%
4 22
 
0.3%
3 51
 
0.6%
2 58
 
0.7%
1 345
 
4.2%
0 7733
94.1%

seq_id
Real number (ℝ)

High correlation 

Distinct918
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean453.7219
Minimum1
Maximum928
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.3 KiB
2025-04-29T11:37:03.853226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile41
Q1211.75
median452
Q3689.25
95-th percentile879.05
Maximum928
Range927
Interquartile range (IQR)477.5

Descriptive statistics

Standard deviation271.578
Coefficient of variation (CV)0.59855608
Kurtosis-1.2331304
Mean453.7219
Median Absolute Deviation (MAD)239
Skewness0.036514788
Sum3729594
Variance73754.611
MonotonicityNot monotonic
2025-04-29T11:37:03.923904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129 11
 
0.1%
99 11
 
0.1%
100 11
 
0.1%
17 11
 
0.1%
18 11
 
0.1%
101 11
 
0.1%
22 11
 
0.1%
126 11
 
0.1%
35 11
 
0.1%
37 11
 
0.1%
Other values (908) 8110
98.7%
ValueCountFrequency (%)
1 10
0.1%
2 11
0.1%
3 11
0.1%
4 11
0.1%
5 11
0.1%
6 10
0.1%
7 11
0.1%
8 11
0.1%
9 11
0.1%
10 8
0.1%
ValueCountFrequency (%)
928 10
0.1%
927 6
0.1%
926 10
0.1%
925 7
0.1%
924 9
0.1%
923 9
0.1%
922 7
0.1%
921 8
0.1%
920 10
0.1%
919 10
0.1%
Distinct1106
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Memory size64.3 KiB
2025-04-29T11:37:04.071774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length26
Median length18
Mean length18.186861
Min length18

Characters and Unicode

Total characters149496
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique204 ?
Unique (%)2.5%

Sample

1st rowseq_gen\00001_seq_gen.puml
2nd rowseq_gen\00002_seq_gen.puml
3rd rowseq_gen\00003_seq_gen.puml
4th rowseq_gen\00004_seq_gen.puml
5th rowseq_gen\00005_seq_gen.puml
ValueCountFrequency (%)
seq\00912_seq.puml 10
 
0.1%
seq\00915_seq.puml 10
 
0.1%
seq\00918_seq.puml 10
 
0.1%
seq\00904_seq.puml 10
 
0.1%
seq\00906_seq.puml 10
 
0.1%
seq\00907_seq.puml 10
 
0.1%
seq\00908_seq.puml 10
 
0.1%
seq\00909_seq.puml 10
 
0.1%
seq\00351_seq.puml 10
 
0.1%
seq\00352_seq.puml 10
 
0.1%
Other values (1096) 8120
98.8%
2025-04-29T11:37:04.287411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 19085
12.8%
e 16824
11.3%
s 16440
11.0%
q 16440
11.0%
_ 8604
 
5.8%
\ 8220
 
5.5%
m 8220
 
5.5%
. 8220
 
5.5%
u 8220
 
5.5%
p 8220
 
5.5%
Other values (12) 31003
20.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 149496
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 19085
12.8%
e 16824
11.3%
s 16440
11.0%
q 16440
11.0%
_ 8604
 
5.8%
\ 8220
 
5.5%
m 8220
 
5.5%
. 8220
 
5.5%
u 8220
 
5.5%
p 8220
 
5.5%
Other values (12) 31003
20.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 149496
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 19085
12.8%
e 16824
11.3%
s 16440
11.0%
q 16440
11.0%
_ 8604
 
5.8%
\ 8220
 
5.5%
m 8220
 
5.5%
. 8220
 
5.5%
u 8220
 
5.5%
p 8220
 
5.5%
Other values (12) 31003
20.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 149496
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 19085
12.8%
e 16824
11.3%
s 16440
11.0%
q 16440
11.0%
_ 8604
 
5.8%
\ 8220
 
5.5%
m 8220
 
5.5%
. 8220
 
5.5%
u 8220
 
5.5%
p 8220
 
5.5%
Other values (12) 31003
20.7%

seq_date
Date

Missing 

Distinct381
Distinct (%)5.1%
Missing766
Missing (%)9.3%
Memory size64.3 KiB
Minimum2013-12-31 00:00:00
Maximum2025-04-28 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-04-29T11:37:04.349576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:37:04.424929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

seq_origin
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size64.3 KiB
github
8028 
chatgpt
 
192

Length

Max length7
Median length6
Mean length6.0233577
Min length6

Characters and Unicode

Total characters49512
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowchatgpt
2nd rowchatgpt
3rd rowchatgpt
4th rowchatgpt
5th rowchatgpt

Common Values

ValueCountFrequency (%)
github 8028
97.7%
chatgpt 192
 
2.3%

Length

2025-04-29T11:37:04.492933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-29T11:37:04.525650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
github 8028
97.7%
chatgpt 192
 
2.3%

Most occurring characters

ValueCountFrequency (%)
t 8412
17.0%
g 8220
16.6%
h 8220
16.6%
i 8028
16.2%
u 8028
16.2%
b 8028
16.2%
c 192
 
0.4%
a 192
 
0.4%
p 192
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49512
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 8412
17.0%
g 8220
16.6%
h 8220
16.6%
i 8028
16.2%
u 8028
16.2%
b 8028
16.2%
c 192
 
0.4%
a 192
 
0.4%
p 192
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49512
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 8412
17.0%
g 8220
16.6%
h 8220
16.6%
i 8028
16.2%
u 8028
16.2%
b 8028
16.2%
c 192
 
0.4%
a 192
 
0.4%
p 192
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49512
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 8412
17.0%
g 8220
16.6%
h 8220
16.6%
i 8028
16.2%
u 8028
16.2%
b 8028
16.2%
c 192
 
0.4%
a 192
 
0.4%
p 192
 
0.4%

seq_source_url
Text

Missing 

Distinct914
Distinct (%)11.4%
Missing192
Missing (%)2.3%
Memory size64.3 KiB
2025-04-29T11:37:04.675703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length288
Median length199
Mean length147.53014
Min length95

Characters and Unicode

Total characters1184372
Distinct characters71
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)0.1%

Sample

1st rowhttps://raw.githubusercontent.com/openshift/lightspeed-service/c790838527bee3dead2842cbe466b1e77f4c4d20/docs/sequence_diagram.uml
2nd rowhttps://raw.githubusercontent.com/openshift/lightspeed-service/c790838527bee3dead2842cbe466b1e77f4c4d20/docs/sequence_diagram.uml
3rd rowhttps://raw.githubusercontent.com/openshift/lightspeed-service/c790838527bee3dead2842cbe466b1e77f4c4d20/docs/sequence_diagram.uml
4th rowhttps://raw.githubusercontent.com/openshift/lightspeed-service/c790838527bee3dead2842cbe466b1e77f4c4d20/docs/sequence_diagram.uml
5th rowhttps://raw.githubusercontent.com/openshift/lightspeed-service/c790838527bee3dead2842cbe466b1e77f4c4d20/docs/sequence_diagram.uml
ValueCountFrequency (%)
https://raw.githubusercontent.com/ryangolpayegani/us2sd_benchmark/a011ff8836936584d7d16493addac0c2479f7b24/benchmark/puml/0321_nsf_31.puml 10
 
0.1%
https://raw.githubusercontent.com/feiskyer/kubernetes-handbook/34a5e0891d431418ff5488112bcf3b2be89ff035/examples/plantuml/sequence.puml 10
 
0.1%
https://raw.githubusercontent.com/aw-birdgang/blockchain-payments/f9fd8de086921868718250b727142c572fc605a5/puml/sequence/sequence-full.puml 10
 
0.1%
https://raw.githubusercontent.com/caade/c3/4bbe48a335b936cf75808d0902b32f73b99ff958/docs/usecases/manage-clouds/create-cloud.puml 10
 
0.1%
https://raw.githubusercontent.com/hyperledger-identus/cloud-agent/efeb15fa86a6a58bb419df3ad3b4366e201b7de1/docs/docusaurus/connections/connection-flow.puml 10
 
0.1%
https://raw.githubusercontent.com/dongthinh2001/project2/c63b75ff45434ad51ec8ecca6d363102c14ad130/plantuml/doimatkhau.puml 10
 
0.1%
https://raw.githubusercontent.com/ryangolpayegani/us2sd_benchmark/a011ff8836936584d7d16493addac0c2479f7b24/benchmark/puml/0339_nsf_49.puml 10
 
0.1%
https://raw.githubusercontent.com/the-lum/puml-themes-gallery/686e38faf550e7c5199db71d0c0518b8c71276b1/gallery/themed-input/sequence-superhero.puml 10
 
0.1%
https://raw.githubusercontent.com/linkenpeng/java/c9993a884f905cee583bec5f4c828df2afc671b6/java-basic/src/main/java/com/intecsec/java/basic/uml/sequence.puml 10
 
0.1%
https://raw.githubusercontent.com/edcance/workcells/5ddf6ba024ae19c1ba6919a49a98a81df5104b4c/diagrams/casos%20de%20uso/manu%20req%20fun/diagramas%20de%20secuencia/superadmin%20agrega%20falta.puml 10
 
0.1%
Other values (904) 7928
98.8%
2025-04-29T11:37:04.909833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 76903
 
6.5%
a 69109
 
5.8%
/ 68760
 
5.8%
t 59826
 
5.1%
c 56143
 
4.7%
n 44827
 
3.8%
s 42152
 
3.6%
r 41423
 
3.5%
o 38087
 
3.2%
u 36728
 
3.1%
Other values (61) 650414
54.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1184372
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 76903
 
6.5%
a 69109
 
5.8%
/ 68760
 
5.8%
t 59826
 
5.1%
c 56143
 
4.7%
n 44827
 
3.8%
s 42152
 
3.6%
r 41423
 
3.5%
o 38087
 
3.2%
u 36728
 
3.1%
Other values (61) 650414
54.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1184372
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 76903
 
6.5%
a 69109
 
5.8%
/ 68760
 
5.8%
t 59826
 
5.1%
c 56143
 
4.7%
n 44827
 
3.8%
s 42152
 
3.6%
r 41423
 
3.5%
o 38087
 
3.2%
u 36728
 
3.1%
Other values (61) 650414
54.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1184372
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 76903
 
6.5%
a 69109
 
5.8%
/ 68760
 
5.8%
t 59826
 
5.1%
c 56143
 
4.7%
n 44827
 
3.8%
s 42152
 
3.6%
r 41423
 
3.5%
o 38087
 
3.2%
u 36728
 
3.1%
Other values (61) 650414
54.9%

seq_repo
Text

Missing 

Distinct458
Distinct (%)5.7%
Missing192
Missing (%)2.3%
Memory size64.3 KiB
2025-04-29T11:37:05.045408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length61
Median length50
Mean length24.869706
Min length7

Characters and Unicode

Total characters199654
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowopenshift/lightspeed-service
2nd rowopenshift/lightspeed-service
3rd rowopenshift/lightspeed-service
4th rowopenshift/lightspeed-service
5th rowopenshift/lightspeed-service
ValueCountFrequency (%)
ryangolpayegani/us2sd_benchmark 1568
 
19.5%
caade/c3 128
 
1.6%
vanquang2002/server 100
 
1.2%
vanquang2002/server_ver1 98
 
1.2%
trongend123/huongsen 95
 
1.2%
ruben1132/pi5_23-24 84
 
1.0%
caade/edgeville 80
 
1.0%
cau-se-its/se_its_back-end 72
 
0.9%
madajaju/sabr 67
 
0.8%
madajaju/edgemere 62
 
0.8%
Other values (448) 5674
70.7%
2025-04-29T11:37:05.259540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 17264
 
8.6%
e 15611
 
7.8%
n 12791
 
6.4%
r 9494
 
4.8%
i 9460
 
4.7%
o 9056
 
4.5%
/ 8028
 
4.0%
l 6778
 
3.4%
t 6619
 
3.3%
- 6305
 
3.2%
Other values (56) 98248
49.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 199654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 17264
 
8.6%
e 15611
 
7.8%
n 12791
 
6.4%
r 9494
 
4.8%
i 9460
 
4.7%
o 9056
 
4.5%
/ 8028
 
4.0%
l 6778
 
3.4%
t 6619
 
3.3%
- 6305
 
3.2%
Other values (56) 98248
49.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 199654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 17264
 
8.6%
e 15611
 
7.8%
n 12791
 
6.4%
r 9494
 
4.8%
i 9460
 
4.7%
o 9056
 
4.5%
/ 8028
 
4.0%
l 6778
 
3.4%
t 6619
 
3.3%
- 6305
 
3.2%
Other values (56) 98248
49.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 199654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 17264
 
8.6%
e 15611
 
7.8%
n 12791
 
6.4%
r 9494
 
4.8%
i 9460
 
4.7%
o 9056
 
4.5%
/ 8028
 
4.0%
l 6778
 
3.4%
t 6619
 
3.3%
- 6305
 
3.2%
Other values (56) 98248
49.2%

seq_ai_model
Categorical

Constant  Missing 

Distinct1
Distinct (%)0.5%
Missing8028
Missing (%)97.7%
Memory size64.3 KiB
gpt-3.5-turbo-0125
192 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters3456
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgpt-3.5-turbo-0125
2nd rowgpt-3.5-turbo-0125
3rd rowgpt-3.5-turbo-0125
4th rowgpt-3.5-turbo-0125
5th rowgpt-3.5-turbo-0125

Common Values

ValueCountFrequency (%)
gpt-3.5-turbo-0125 192
 
2.3%
(Missing) 8028
97.7%

Length

2025-04-29T11:37:05.308209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-29T11:37:05.336244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
gpt-3.5-turbo-0125 192
100.0%

Most occurring characters

ValueCountFrequency (%)
- 576
16.7%
t 384
11.1%
5 384
11.1%
p 192
 
5.6%
3 192
 
5.6%
g 192
 
5.6%
. 192
 
5.6%
u 192
 
5.6%
r 192
 
5.6%
b 192
 
5.6%
Other values (4) 768
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3456
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 576
16.7%
t 384
11.1%
5 384
11.1%
p 192
 
5.6%
3 192
 
5.6%
g 192
 
5.6%
. 192
 
5.6%
u 192
 
5.6%
r 192
 
5.6%
b 192
 
5.6%
Other values (4) 768
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3456
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 576
16.7%
t 384
11.1%
5 384
11.1%
p 192
 
5.6%
3 192
 
5.6%
g 192
 
5.6%
. 192
 
5.6%
u 192
 
5.6%
r 192
 
5.6%
b 192
 
5.6%
Other values (4) 768
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3456
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 576
16.7%
t 384
11.1%
5 384
11.1%
p 192
 
5.6%
3 192
 
5.6%
g 192
 
5.6%
. 192
 
5.6%
u 192
 
5.6%
r 192
 
5.6%
b 192
 
5.6%
Other values (4) 768
22.2%

seq_temperature
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.023017032
Minimum0
Maximum1.4
Zeros8028
Zeros (%)97.7%
Negative0
Negative (%)0.0%
Memory size64.3 KiB
2025-04-29T11:37:05.362416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1.4
Range1.4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.15161265
Coefficient of variation (CV)6.5869765
Kurtosis43.787626
Mean0.023017032
Median Absolute Deviation (MAD)0
Skewness6.6574626
Sum189.2
Variance0.022986395
MonotonicityNot monotonic
2025-04-29T11:37:05.401872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 8028
97.7%
1 119
 
1.4%
0.6 20
 
0.2%
0.8 20
 
0.2%
1.2 20
 
0.2%
1.4 13
 
0.2%
ValueCountFrequency (%)
0 8028
97.7%
0.6 20
 
0.2%
0.8 20
 
0.2%
1 119
 
1.4%
1.2 20
 
0.2%
1.4 13
 
0.2%
ValueCountFrequency (%)
1.4 13
 
0.2%
1.2 20
 
0.2%
1 119
 
1.4%
0.8 20
 
0.2%
0.6 20
 
0.2%
0 8028
97.7%

seq_top_p
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.018491484
Minimum0
Maximum1
Zeros8028
Zeros (%)97.7%
Negative0
Negative (%)0.0%
Memory size64.3 KiB
2025-04-29T11:37:05.443872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.12729905
Coefficient of variation (CV)6.8841987
Kurtosis49.919159
Mean0.018491484
Median Absolute Deviation (MAD)0
Skewness7.1123374
Sum152
Variance0.016205049
MonotonicityNot monotonic
2025-04-29T11:37:05.487875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 8028
97.7%
1 112
 
1.4%
0.2 20
 
0.2%
0.4 20
 
0.2%
0.6 20
 
0.2%
0.8 20
 
0.2%
ValueCountFrequency (%)
0 8028
97.7%
0.2 20
 
0.2%
0.4 20
 
0.2%
0.6 20
 
0.2%
0.8 20
 
0.2%
1 112
 
1.4%
ValueCountFrequency (%)
1 112
 
1.4%
0.8 20
 
0.2%
0.6 20
 
0.2%
0.4 20
 
0.2%
0.2 20
 
0.2%
0 8028
97.7%

seq_actors
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0821168
Minimum0
Maximum7
Zeros581
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size64.3 KiB
2025-04-29T11:37:05.525063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile2
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.61410314
Coefficient of variation (CV)0.56750172
Kurtosis26.090977
Mean1.0821168
Median Absolute Deviation (MAD)0
Skewness3.5832328
Sum8895
Variance0.37712266
MonotonicityNot monotonic
2025-04-29T11:37:05.571012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 6763
82.3%
2 641
 
7.8%
0 581
 
7.1%
3 164
 
2.0%
4 37
 
0.5%
7 20
 
0.2%
5 14
 
0.2%
ValueCountFrequency (%)
0 581
 
7.1%
1 6763
82.3%
2 641
 
7.8%
3 164
 
2.0%
4 37
 
0.5%
5 14
 
0.2%
7 20
 
0.2%
ValueCountFrequency (%)
7 20
 
0.2%
5 14
 
0.2%
4 37
 
0.5%
3 164
 
2.0%
2 641
 
7.8%
1 6763
82.3%
0 581
 
7.1%

seq_num_of_participats
Real number (ℝ)

High correlation 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1655718
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.3 KiB
2025-04-29T11:37:05.616010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q36
95-th percentile9
Maximum19
Range18
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.3696497
Coefficient of variation (CV)0.4587391
Kurtosis5.6268769
Mean5.1655718
Median Absolute Deviation (MAD)1
Skewness1.7333204
Sum42461
Variance5.6152399
MonotonicityNot monotonic
2025-04-29T11:37:05.660011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
4 2018
24.5%
5 1633
19.9%
3 1017
12.4%
6 978
11.9%
7 916
11.1%
2 689
 
8.4%
8 347
 
4.2%
9 243
 
3.0%
10 121
 
1.5%
11 86
 
1.0%
Other values (6) 172
 
2.1%
ValueCountFrequency (%)
1 10
 
0.1%
2 689
 
8.4%
3 1017
12.4%
4 2018
24.5%
5 1633
19.9%
6 978
11.9%
7 916
11.1%
8 347
 
4.2%
9 243
 
3.0%
10 121
 
1.5%
ValueCountFrequency (%)
19 29
 
0.4%
16 20
 
0.2%
14 65
 
0.8%
13 29
 
0.4%
12 19
 
0.2%
11 86
 
1.0%
10 121
 
1.5%
9 243
 
3.0%
8 347
 
4.2%
7 916
11.1%

seq_title
Text

Missing 

Distinct74
Distinct (%)13.2%
Missing7659
Missing (%)93.2%
Memory size64.3 KiB
2025-04-29T11:37:05.839525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length81
Median length49
Mean length29.670232
Min length4

Characters and Unicode

Total characters16645
Distinct characters99
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)2.3%

Sample

1st rowRiver Search and Rescue
2nd rowRiver_Search_and_Rescue
3rd rowRiver Search and Rescue
4th rowRiver Search and Rescue
5th rowRiver Search and Rescue
ValueCountFrequency (%)
cactus\nsequence 51
 
2.4%
hyperledger 51
 
2.4%
and 44
 
2.1%
register 40
 
1.9%
39
 
1.8%
transaction 37
 
1.7%
endpoint 34
 
1.6%
flow 30
 
1.4%
staking 29
 
1.4%
method 27
 
1.3%
Other values (198) 1749
82.1%
2025-04-29T11:37:06.077213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1675
 
10.1%
1570
 
9.4%
n 1206
 
7.2%
t 1084
 
6.5%
a 1078
 
6.5%
i 941
 
5.7%
o 874
 
5.3%
r 849
 
5.1%
s 659
 
4.0%
c 569
 
3.4%
Other values (89) 6140
36.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16645
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1675
 
10.1%
1570
 
9.4%
n 1206
 
7.2%
t 1084
 
6.5%
a 1078
 
6.5%
i 941
 
5.7%
o 874
 
5.3%
r 849
 
5.1%
s 659
 
4.0%
c 569
 
3.4%
Other values (89) 6140
36.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16645
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1675
 
10.1%
1570
 
9.4%
n 1206
 
7.2%
t 1084
 
6.5%
a 1078
 
6.5%
i 941
 
5.7%
o 874
 
5.3%
r 849
 
5.1%
s 659
 
4.0%
c 569
 
3.4%
Other values (89) 6140
36.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16645
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1675
 
10.1%
1570
 
9.4%
n 1206
 
7.2%
t 1084
 
6.5%
a 1078
 
6.5%
i 941
 
5.7%
o 874
 
5.3%
r 849
 
5.1%
s 659
 
4.0%
c 569
 
3.4%
Other values (89) 6140
36.9%

seq_num_of_alt
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.42396594
Minimum0
Maximum12
Zeros6292
Zeros (%)76.5%
Negative0
Negative (%)0.0%
Memory size64.3 KiB
2025-04-29T11:37:06.121459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0489584
Coefficient of variation (CV)2.4741573
Kurtosis31.953188
Mean0.42396594
Median Absolute Deviation (MAD)0
Skewness4.5647979
Sum3485
Variance1.1003137
MonotonicityNot monotonic
2025-04-29T11:37:06.169125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 6292
76.5%
1 1157
 
14.1%
2 418
 
5.1%
3 174
 
2.1%
4 89
 
1.1%
5 38
 
0.5%
6 25
 
0.3%
12 10
 
0.1%
10 9
 
0.1%
8 8
 
0.1%
ValueCountFrequency (%)
0 6292
76.5%
1 1157
 
14.1%
2 418
 
5.1%
3 174
 
2.1%
4 89
 
1.1%
5 38
 
0.5%
6 25
 
0.3%
8 8
 
0.1%
10 9
 
0.1%
12 10
 
0.1%
ValueCountFrequency (%)
12 10
 
0.1%
10 9
 
0.1%
8 8
 
0.1%
6 25
 
0.3%
5 38
 
0.5%
4 89
 
1.1%
3 174
 
2.1%
2 418
 
5.1%
1 1157
 
14.1%
0 6292
76.5%

seq_num_of_opt
Categorical

Imbalance 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size64.3 KiB
0
8067 
1
 
107
4
 
20
3
 
16
5
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8220
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row3
3rd row3
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8067
98.1%
1 107
 
1.3%
4 20
 
0.2%
3 16
 
0.2%
5 10
 
0.1%

Length

2025-04-29T11:37:06.219139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-29T11:37:06.254125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 8067
98.1%
1 107
 
1.3%
4 20
 
0.2%
3 16
 
0.2%
5 10
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 8067
98.1%
1 107
 
1.3%
4 20
 
0.2%
3 16
 
0.2%
5 10
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8220
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 8067
98.1%
1 107
 
1.3%
4 20
 
0.2%
3 16
 
0.2%
5 10
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8220
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 8067
98.1%
1 107
 
1.3%
4 20
 
0.2%
3 16
 
0.2%
5 10
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8220
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 8067
98.1%
1 107
 
1.3%
4 20
 
0.2%
3 16
 
0.2%
5 10
 
0.1%

seq_num_of_loop
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.079075426
Minimum0
Maximum8
Zeros7757
Zeros (%)94.4%
Negative0
Negative (%)0.0%
Memory size64.3 KiB
2025-04-29T11:37:06.291138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.42672051
Coefficient of variation (CV)5.3963733
Kurtosis150.62935
Mean0.079075426
Median Absolute Deviation (MAD)0
Skewness10.275896
Sum650
Variance0.1820904
MonotonicityNot monotonic
2025-04-29T11:37:06.337527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 7757
94.4%
1 384
 
4.7%
2 35
 
0.4%
4 19
 
0.2%
3 16
 
0.2%
8 9
 
0.1%
ValueCountFrequency (%)
0 7757
94.4%
1 384
 
4.7%
2 35
 
0.4%
3 16
 
0.2%
4 19
 
0.2%
8 9
 
0.1%
ValueCountFrequency (%)
8 9
 
0.1%
4 19
 
0.2%
3 16
 
0.2%
2 35
 
0.4%
1 384
 
4.7%
0 7757
94.4%

Interactions

2025-04-29T11:37:00.327385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:49.205452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:50.066874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:50.963444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:51.787176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:52.710001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:53.510564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:54.430663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:55.215601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:56.013184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:56.929151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:57.724755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:58.612597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:59.436804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:37:00.387789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:49.273927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:50.196385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:51.023382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:51.847995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:52.771806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:53.570515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:54.489046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:55.272550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:56.158441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:56.991811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:57.782249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:58.673601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:59.496333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:37:00.447963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:49.337311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:50.254676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:51.083874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:51.912266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:52.830233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:53.632767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:54.548994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:55.328235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:56.216563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:57.046752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:57.843894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:58.733546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:59.552764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:37:00.504649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:49.397398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:50.309683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:51.135939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:51.969991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:52.887895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:53.690122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:54.605037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:55.382801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:56.272499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:57.099916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:57.901890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:58.789551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:59.607835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:37:00.569688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:49.460729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:50.370318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:51.198777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:52.115110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:52.943847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:53.758132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:54.662880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:55.437278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:56.334876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:57.157379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:57.969124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:58.850216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:59.665764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:37:00.628688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:49.523864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:50.427240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:51.256723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:52.169184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:53.015030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:53.818100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:54.716398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:55.489463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:56.391248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:57.211379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:58.103790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:58.907805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:59.720538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:37:00.692224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:49.590478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:50.490517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:51.324994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:52.233023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:53.072674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:53.878740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:54.775136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:55.546562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:56.456122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:57.271382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:58.163618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:58.970173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:59.780591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:37:00.749738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:49.651265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:50.548228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:51.379899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:52.291018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:53.126071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:53.934266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:54.827081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:55.598338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:56.512268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:57.326422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:58.215613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:59.026537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-29T11:37:00.804971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:49.705490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:50.605155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:51.434709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:52.349623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:53.178121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:54.074788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:54.880618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:55.660084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:56.571382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:57.381212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:58.270792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:59.081923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:59.888016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:37:00.865040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:49.767391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:50.664101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:51.490939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:52.410018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:53.234126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:54.135005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:54.940090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:55.727386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:56.631603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:57.442125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:58.328041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:59.144976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:59.946386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:37:00.922484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:49.824305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:50.721190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:51.545642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:52.468992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:53.287345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:54.193169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:54.992446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:55.782386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:56.687497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:57.497453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:58.383747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:59.204698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:37:00.003386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:37:00.979701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:49.884421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:50.779714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:51.604641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:52.529047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:53.341509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:54.251070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:55.048608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:55.840636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:56.746694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:57.554900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:58.436739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:59.261968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:37:00.157850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:37:01.044630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:49.944655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:50.841658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:51.670563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:52.589977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:53.399481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:54.310373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:55.105179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:55.900168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:56.808213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:57.612510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:58.498081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:59.318913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:37:00.215844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:37:01.100840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:50.005661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:50.899477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:51.726231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:52.650212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:53.453480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:54.367663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:55.158990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:55.955044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:56.870178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:57.666447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:58.554597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:36:59.375006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-29T11:37:00.270508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-04-29T11:37:06.477809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
seq_actorsseq_idseq_num_of_altseq_num_of_loopseq_num_of_optseq_num_of_participatsseq_originseq_temperatureseq_top_puc_actorsuc_iduc_num_of_alt_scenarionsuc_num_of_err_scenarionsuc_num_of_stepsuc_originuc_source_urluc_temperatureuc_top_p
seq_actors1.000-0.0080.1220.0330.0480.0560.3490.1400.1400.1270.0030.0470.0820.1040.3490.633-0.043-0.038
seq_id-0.0081.000-0.014-0.0060.100-0.0130.277-0.206-0.206-0.0270.832-0.045-0.1370.0130.2770.7370.0610.063
seq_num_of_alt0.122-0.0141.0000.1670.1430.0430.016-0.035-0.0350.1220.0680.3670.1500.1040.0160.378-0.022-0.006
seq_num_of_loop0.033-0.0060.1671.0000.3800.0820.019-0.031-0.0310.1260.0210.013-0.0190.1070.0190.0000.0030.011
seq_num_of_opt0.0480.1000.1430.3801.0000.1460.1340.0830.0700.0930.0980.0230.1230.0580.1340.3880.0640.063
seq_num_of_participats0.056-0.0130.0430.0820.1461.0000.117-0.090-0.0900.330-0.0140.011-0.0370.4600.1170.5100.0220.021
seq_origin0.3490.2770.0160.0190.1340.1171.0001.0001.0000.2730.4150.1510.7530.0170.9971.0001.0001.000
seq_temperature0.140-0.206-0.035-0.0310.083-0.0901.0001.0001.0000.017-0.2610.0950.5950.0161.0000.000-0.296-0.292
seq_top_p0.140-0.206-0.035-0.0310.070-0.0901.0001.0001.0000.017-0.2610.0950.5950.0161.0000.000-0.296-0.292
uc_actors0.127-0.0270.1220.1260.0930.3300.2730.0170.0171.000-0.0240.0130.0250.3230.2730.951-0.041-0.058
uc_id0.0030.8320.0680.0210.098-0.0140.415-0.261-0.261-0.0241.000-0.000-0.1350.0070.4151.0000.1360.144
uc_num_of_alt_scenarions0.047-0.0450.3670.0130.0230.0110.1510.0950.0950.013-0.0001.0000.516-0.0810.1510.954-0.031-0.013
uc_num_of_err_scenarions0.082-0.1370.150-0.0190.123-0.0370.7530.5950.5950.025-0.1350.5161.000-0.0420.7530.962-0.167-0.152
uc_num_of_steps0.1040.0130.1040.1070.0580.4600.0170.0160.0160.3230.007-0.081-0.0421.0000.0170.951-0.047-0.053
uc_origin0.3490.2770.0160.0190.1340.1170.9971.0001.0000.2730.4150.1510.7530.0171.0001.0001.0001.000
uc_source_url0.6330.7370.3780.0000.3880.5101.0000.0000.0000.9511.0000.9540.9620.9511.0001.0001.0001.000
uc_temperature-0.0430.061-0.0220.0030.0640.0221.000-0.296-0.296-0.0410.136-0.031-0.167-0.0471.0001.0001.0000.039
uc_top_p-0.0380.063-0.0060.0110.0630.0211.000-0.292-0.292-0.0580.144-0.013-0.152-0.0531.0001.0000.0391.000

Missing values

2025-04-29T11:37:01.221139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-29T11:37:01.364953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-04-29T11:37:01.525729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

nameoriginuc_iduc_fileuc_dateuc_originuc_source_urluc_repouc_ai_modeluc_temperatureuc_top_puc_titleuc_actorsuc_num_of_stepsuc_num_of_alt_scenarionsuc_num_of_err_scenarionsseq_idseq_fileseq_dateseq_originseq_source_urlseq_reposeq_ai_modelseq_temperatureseq_top_pseq_actorsseq_num_of_participatsseq_titleseq_num_of_altseq_num_of_optseq_num_of_loop
0River Search and Rescuesemi-synthetic1uc\00001_uc.xml2020-12-19githubhttps://raw.githubusercontent.com/SAREC-Lab/sUAS-UseCases/refs/heads/SPLC-2020/usecases/main/RiverRescue.mdSAREC-Lab/sUAS-UseCasesNaN0.00.0River Search and Rescue216131seq_gen\00001_seq_gen.puml2025-04-27chatgptNaNNaNgpt-3.5-turbo-01250.61.008River Search and Rescue000
1River Search and Rescuesemi-synthetic1uc\00001_uc.xml2020-12-19githubhttps://raw.githubusercontent.com/SAREC-Lab/sUAS-UseCases/refs/heads/SPLC-2020/usecases/main/RiverRescue.mdSAREC-Lab/sUAS-UseCasesNaN0.00.0River Search and Rescue216132seq_gen\00002_seq_gen.puml2025-04-27chatgptNaNNaNgpt-3.5-turbo-01251.00.2110NaN130
2River Search and Rescuesemi-synthetic1uc\00001_uc.xml2020-12-19githubhttps://raw.githubusercontent.com/SAREC-Lab/sUAS-UseCases/refs/heads/SPLC-2020/usecases/main/RiverRescue.mdSAREC-Lab/sUAS-UseCasesNaN0.00.0River Search and Rescue216133seq_gen\00003_seq_gen.puml2025-04-27chatgptNaNNaNgpt-3.5-turbo-01250.81.0011River_Search_and_Rescue130
3River Search and Rescuesemi-synthetic1uc\00001_uc.xml2020-12-19githubhttps://raw.githubusercontent.com/SAREC-Lab/sUAS-UseCases/refs/heads/SPLC-2020/usecases/main/RiverRescue.mdSAREC-Lab/sUAS-UseCasesNaN0.00.0River Search and Rescue216134seq_gen\00004_seq_gen.puml2025-04-27chatgptNaNNaNgpt-3.5-turbo-01251.00.407NaN000
4River Search and Rescuesemi-synthetic1uc\00001_uc.xml2020-12-19githubhttps://raw.githubusercontent.com/SAREC-Lab/sUAS-UseCases/refs/heads/SPLC-2020/usecases/main/RiverRescue.mdSAREC-Lab/sUAS-UseCasesNaN0.00.0River Search and Rescue216135seq_gen\00005_seq_gen.puml2025-04-27chatgptNaNNaNgpt-3.5-turbo-01251.01.006River Search and Rescue000
5River Search and Rescuesemi-synthetic1uc\00001_uc.xml2020-12-19githubhttps://raw.githubusercontent.com/SAREC-Lab/sUAS-UseCases/refs/heads/SPLC-2020/usecases/main/RiverRescue.mdSAREC-Lab/sUAS-UseCasesNaN0.00.0River Search and Rescue216136seq_gen\00006_seq_gen.puml2025-04-27chatgptNaNNaNgpt-3.5-turbo-01251.00.6110NaN130
6River Search and Rescuesemi-synthetic1uc\00001_uc.xml2020-12-19githubhttps://raw.githubusercontent.com/SAREC-Lab/sUAS-UseCases/refs/heads/SPLC-2020/usecases/main/RiverRescue.mdSAREC-Lab/sUAS-UseCasesNaN0.00.0River Search and Rescue216137seq_gen\00007_seq_gen.puml2025-04-27chatgptNaNNaNgpt-3.5-turbo-01251.21.028River Search and Rescue000
7River Search and Rescuesemi-synthetic1uc\00001_uc.xml2020-12-19githubhttps://raw.githubusercontent.com/SAREC-Lab/sUAS-UseCases/refs/heads/SPLC-2020/usecases/main/RiverRescue.mdSAREC-Lab/sUAS-UseCasesNaN0.00.0River Search and Rescue216138seq_gen\00008_seq_gen.puml2025-04-27chatgptNaNNaNgpt-3.5-turbo-01251.00.822River Search and Rescue000
8River Search and Rescuesemi-synthetic1uc\00001_uc.xml2020-12-19githubhttps://raw.githubusercontent.com/SAREC-Lab/sUAS-UseCases/refs/heads/SPLC-2020/usecases/main/RiverRescue.mdSAREC-Lab/sUAS-UseCasesNaN0.00.0River Search and Rescue216139seq_gen\00009_seq_gen.puml2025-04-27chatgptNaNNaNgpt-3.5-turbo-01251.01.009River Search and Rescue130
9Deliver item to a specific locationsemi-synthetic2uc\00002_uc.xml2020-12-19githubhttps://raw.githubusercontent.com/SAREC-Lab/sUAS-UseCases/refs/heads/SPLC-2020/usecases/main/ItemDelivery.mdSAREC-Lab/sUAS-UseCasesNaN0.00.0Deliver item to a specific location392110seq_gen\00010_seq_gen.puml2025-04-27chatgptNaNNaNgpt-3.5-turbo-01250.61.033Deliver item to a specific location000
nameoriginuc_iduc_fileuc_dateuc_originuc_source_urluc_repouc_ai_modeluc_temperatureuc_top_puc_titleuc_actorsuc_num_of_stepsuc_num_of_alt_scenarionsuc_num_of_err_scenarionsseq_idseq_fileseq_dateseq_originseq_source_urlseq_reposeq_ai_modelseq_temperatureseq_top_pseq_actorsseq_num_of_participatsseq_titleseq_num_of_altseq_num_of_optseq_num_of_loop
8210Prototype Developmentsemi-synthetic8019uc_gen\08019_uc_gen.xml2025-04-29chatgptNaNNaNgpt-3.5-turbo-01251.21.0Prototype Development1900922seq\00922_seq.puml2025-01-29githubhttps://raw.githubusercontent.com/RyanGolpayegani/US2SD_Benchmark/a011ff8836936584d7d16493addac0c2479f7b24/Benchmark/PUML/0331_nsf_41.pumlRyanGolpayegani/US2SD_BenchmarkNaN0.00.015NaN000
8211Edit Embargo Lengthsemi-synthetic8020uc_gen\08020_uc_gen.xml2025-04-29chatgptNaNNaNgpt-3.5-turbo-01250.61.0Edit Embargo Length4520923seq\00923_seq.puml2025-01-29githubhttps://raw.githubusercontent.com/RyanGolpayegani/US2SD_Benchmark/a011ff8836936584d7d16493addac0c2479f7b24/Benchmark/PUML/1580_mis_28.pumlRyanGolpayegani/US2SD_BenchmarkNaN0.00.014NaN100
8212Edit Embargo Lengthsemi-synthetic8021uc_gen\08021_uc_gen.xml2025-04-29chatgptNaNNaNgpt-3.5-turbo-01251.00.4Edit Embargo Length11110923seq\00923_seq.puml2025-01-29githubhttps://raw.githubusercontent.com/RyanGolpayegani/US2SD_Benchmark/a011ff8836936584d7d16493addac0c2479f7b24/Benchmark/PUML/1580_mis_28.pumlRyanGolpayegani/US2SD_BenchmarkNaN0.00.014NaN100
8213Edit Embargo Lengthsemi-synthetic8022uc_gen\08022_uc_gen.xml2025-04-29chatgptNaNNaNgpt-3.5-turbo-01251.21.0Edit Embargo Length11220923seq\00923_seq.puml2025-01-29githubhttps://raw.githubusercontent.com/RyanGolpayegani/US2SD_Benchmark/a011ff8836936584d7d16493addac0c2479f7b24/Benchmark/PUML/1580_mis_28.pumlRyanGolpayegani/US2SD_BenchmarkNaN0.00.014NaN100
8214Manage Research Outputsemi-synthetic8023uc_gen\08023_uc_gen.xml2025-04-29chatgptNaNNaNgpt-3.5-turbo-01250.81.0Manage Research Output41200924seq\00924_seq.puml2025-01-29githubhttps://raw.githubusercontent.com/RyanGolpayegani/US2SD_Benchmark/a011ff8836936584d7d16493addac0c2479f7b24/Benchmark/PUML/1370_rdadmp_37.pumlRyanGolpayegani/US2SD_BenchmarkNaN0.00.014NaN100
8215Demonstration Preparationsemi-synthetic8024uc_gen\08024_uc_gen.xml2025-04-29chatgptNaNNaNgpt-3.5-turbo-01250.61.0Demonstration Preparation4400925seq\00925_seq.puml2025-01-29githubhttps://raw.githubusercontent.com/RyanGolpayegani/US2SD_Benchmark/a011ff8836936584d7d16493addac0c2479f7b24/Benchmark/PUML/0350_nsf_60.pumlRyanGolpayegani/US2SD_BenchmarkNaN0.00.016NaN000
8216Demonstration Preparationsemi-synthetic8025uc_gen\08025_uc_gen.xml2025-04-29chatgptNaNNaNgpt-3.5-turbo-01251.41.0Demonstration Preparation3400925seq\00925_seq.puml2025-01-29githubhttps://raw.githubusercontent.com/RyanGolpayegani/US2SD_Benchmark/a011ff8836936584d7d16493addac0c2479f7b24/Benchmark/PUML/0350_nsf_60.pumlRyanGolpayegani/US2SD_BenchmarkNaN0.00.016NaN000
8217Grant Submit Permissionssemi-synthetic8026uc_gen\08026_uc_gen.xml2025-04-29chatgptNaNNaNgpt-3.5-turbo-01251.00.2Grant Submit Permissions4800927seq\00927_seq.puml2025-01-29githubhttps://raw.githubusercontent.com/RyanGolpayegani/US2SD_Benchmark/a011ff8836936584d7d16493addac0c2479f7b24/Benchmark/PUML/1620_mis_68.pumlRyanGolpayegani/US2SD_BenchmarkNaN0.00.014NaN000
8218Managing Permissionssemi-synthetic8027uc_gen\08027_uc_gen.xml2025-04-29chatgptNaNNaNgpt-3.5-turbo-01251.21.0Managing Permissions42200927seq\00927_seq.puml2025-01-29githubhttps://raw.githubusercontent.com/RyanGolpayegani/US2SD_Benchmark/a011ff8836936584d7d16493addac0c2479f7b24/Benchmark/PUML/1620_mis_68.pumlRyanGolpayegani/US2SD_BenchmarkNaN0.00.014NaN000
8219Grant Submit Permissionssemi-synthetic8028uc_gen\08028_uc_gen.xml2025-04-29chatgptNaNNaNgpt-3.5-turbo-01251.41.0Grant Submit Permissions4800927seq\00927_seq.puml2025-01-29githubhttps://raw.githubusercontent.com/RyanGolpayegani/US2SD_Benchmark/a011ff8836936584d7d16493addac0c2479f7b24/Benchmark/PUML/1620_mis_68.pumlRyanGolpayegani/US2SD_BenchmarkNaN0.00.014NaN000